Background and Objective: Optimum number of air quality monitoring stations in Mashhad is an essential task for management of the urban environment. However, real monitoring and accurate information on the status of air quality require proper spatial distribution of air quality monitoring stations in the city of Mashhad. The aim of the present study was to determine optimum site locations for air quality monitoring, including Downtown Pedestrain Exposure Station, Downtown Background Exposure Station, and Residential Population Exposure Station by three Multiple-Criteria Decision-Making (MCDM) techniques.

Materials and Methods: In the precent study, sites for new air quality monitoring stations t in Mashhad were determined based on a proposed protocol in the United States. Accordingly, the criteria effective for site selection such as population density, distance from existing stations, vicinity to vegitation, vehicle density and other factors were used by applying Analytic Hierarchy Process (AHP), Fuzzy set, and Probability Density Function (PDF).

Results: Location similarity of the sites proposed by decision making methods was evaluated to know its reliability. The compactness of distribution of the proposed sites were compared by applying spatial statistic methods auch as Average Nearest Neighbor (ANN) and Standard. The results from ANN indicated that fuzzy set mapped the suggested sites was fully scattered within the entire city of Mashhad and was statistically significant at 99% confidence level. The PDF method also offered the same spatial pattern as fuzzy set. Overall results of this study indicated spatial optimization of suggested sites location for fuzzy set and PDF.

Conclusion: The overall results of the decision-making methods used in this study indicated that it is necessary to increase number of air quality monitoring stations at Northwest of Mashhad due to the urban growth in the city. The results also showd the possibility of using Probability Density Function (PDF) as a method of decision-making in GIS for locating and ranking of new air quality monitoring stations.